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Record W2943286760 · doi:10.4018/ijoci.2019070102

Open Business Models for Business Technology Management Bodies of Knowledge

2019· article· en· W2943286760 on OpenAlexaff
Svetlana Sidenko, Raul Valverde, Stéphane Gagnon

Bibliographic record

VenueInternational Journal of Organizational and Collective Intelligence · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsConcordia UniversityUniversité du Québec en Outaouais
Fundersnot available
KeywordsCurriculumKnowledge managementEngineering ethicsBody of knowledgeDisciplineComputer scienceSociologyPedagogyEngineeringSocial science

Abstract

fetched live from OpenAlex

The information systems (IS) discipline is in an ongoing crisis because of several reasons. Learning institutions do not have a well-defined curriculum on what should be taught in the IS discipline. Recently, there are many organizations and researchers who have come up with different BoKs in the IS discipline as discussed in this article. Additionally, IS discipline does not have a universal body of knowledge like in other fields such as electrical engineering which makes it even more challenging to align the existing industry knowledge and academic curriculum. Therefore, there is a need to develop a unified knowledge framework in the IS discipline which offers a single source of reference in both professional sector and development of academic curriculum. The major challenge in the developing of a unified knowledge framework in the IS discipline is that it is changing and evolving rapidly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0050.010
Scholarly communication0.0210.025
Open science0.0030.015
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0220.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.304
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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